AI & Data Classification

Human & AI-based data classification services for text, video, audio and image to enable businesses to organize their data as per importance, sensitivity, and potential application.

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Image Annotation

Helping AI with training data derived from X-rays, CT scans, MRIs, etc., for AI-powered models and applications to improve medical practice, clinical management, and healthcare delivery.

Video Annotation

Bolstering virtual delivery and learning through classification, categorization, and segmentation of regions of interest in videos such as recorded surgical procedures.

Audio Annotation

Harnessing NLP expertise to appropriately annotate & label audio data for developing voice-assistant systems for smooth clinical practice and to infuse AI in other voice-enabled products.

Waveform Annotation

Using data annotation & labeling to classify features and segment regions of interests within waveforms for training predictive algorithms & AI models that detect acute heart diseases and neurological conditions.

AI Medical Coding

Medical coding involves the conversion of healthcare diagnoses, procedures, medical services, and equipment into universal medical alphanumeric codes. Codes for diagnoses and procedures are derived from medical records.

Text Annotation

Quickly organize and enrich large amounts of text data for developing AI with accurately annotated & labeled text strings, hand-written notes, prescriptions, insurance verification reports, etc.

2D Bounding Boxes

It defines the boundaries of objects in a two-dimensional space using graphic representations. The boxes are usually used in computer vision and ML applications for separating areas of interest for objects.

Object Detection

This deals with detection of instances of semantic objects pertaining to a certain class (humans, buildings, or cars) with respect to digital images and videos.

Key Point Annotation

The Key Point image data annotation recognizes facial gestures, human poses, expressions, emotions, body language, and sentiments through the connection of multiple dots.

Polygon Annotation

This involves marking and drawing shapes on a digital image. It allows marking objects within an image based on their position and orientation. It involves labeling images of irregular dimensions.

3D Cuboid Annotation

This is used for detecting and recognizing 3D objects in images. It helps machines in determining the depth of objects like vehicles, people, buildings, and other objects.

Semantic Segmentation

A semantic segmentation technique is used in computer vision to segment images. An image dataset is semantically segmented to locate all categories and classes.

Image Classification

Images or objects are classified within images as per custom multi-level taxonomies like land use, crops, etc. It converts image data into image insights for AI and ML models.

Skeletal Annotation

This is used to highlight body movement and alignment. Annotators connect lines on the human body through this technique. They connect them with dots at points of articulation.

Polygons Annotation

The purpose of polygon annotation is to draw polygons around objects of interest in an image or video. This is done by drawing the boundaries of objects in an image or video, such as vehicles, people, and buildings

Events Classification

This involves the process of manually adding relevant labels or tags to video clips that are related to a particular event or class. The annotation process is performed by manually creating bounding boxes around objects in an image or video frame and labeling them with the relevant class.

Event Tracking

The datasets built upon video annotation can be used to create an event-tracking system that provides valuable insights into user behavior, such as how often people visit a certain location or how frequently a certain type of event occurs.